[ Topic ]
Evals, observability, cost and reliability at scale.
Practical techniques for using AI coding assistants on legacy and brownfield codebases — giving enough context, preventing over-modernization, and validating changes that can't break.
A practical workflow for feeding production logs to an LLM to surface root causes, correlate errors, and cut mean-time-to-resolution — without leaking sensitive data.
A practical guide to using AI coding assistants to write, review, and validate schema migrations — catching destructive operations before they reach production.
A practical guide to sharing the right context with AI assistants without exposing API keys, credentials, and sensitive data that creates security risk.
How to structure AI-assisted performance work — what profiling data to share, how to frame bottlenecks, and how to validate that a suggested fix actually helps.
How to prompt AI coding assistants to generate tests that actually catch bugs — edge cases, failure modes, and boundary conditions, not just happy-path boilerplate.
A practical checklist for reviewing AI-written code — catching hallucinated APIs, subtle logic bugs, and security gaps before they reach production.